1,255 research outputs found
Optimal Hour-Ahead Bidding in the Real-Time Electricity Market with Battery Storage using Approximate Dynamic Programming
There is growing interest in the use of grid-level storage to smooth
variations in supply that are likely to arise with increased use of wind and
solar energy. Energy arbitrage, the process of buying, storing, and selling
electricity to exploit variations in electricity spot prices, is becoming an
important way of paying for expensive investments into grid-level storage.
Independent system operators such as the NYISO (New York Independent System
Operator) require that battery storage operators place bids into an hour-ahead
market (although settlements may occur in increments as small as 5 minutes,
which is considered near "real-time"). The operator has to place these bids
without knowing the energy level in the battery at the beginning of the hour,
while simultaneously accounting for the value of leftover energy at the end of
the hour. The problem is formulated as a dynamic program. We describe and
employ a convergent approximate dynamic programming (ADP) algorithm that
exploits monotonicity of the value function to find a revenue-generating
bidding policy; using optimal benchmarks, we empirically show the computational
benefits of the algorithm. Furthermore, we propose a distribution-free variant
of the ADP algorithm that does not require any knowledge of the distribution of
the price process (and makes no assumptions regarding a specific real-time
price model). We demonstrate that a policy trained on historical real-time
price data from the NYISO using this distribution-free approach is indeed
effective.Comment: 28 pages, 11 figure
Transferable Energy Storage Bidder
Energy storage resources must consider both price uncertainties and their
physical operating characteristics when participating in wholesale electricity
markets. This is a challenging problem as electricity prices are highly
volatile, and energy storage has efficiency losses, power, and energy
constraints. This paper presents a novel, versatile, and transferable approach
combining model-based optimization with a convolutional long short-term memory
network for energy storage to respond to or bid into wholesale electricity
markets. We test our proposed approach using historical prices from New York
State, showing it achieves state-of-the-art results, achieving between 70% to
near 90% profit ratio compared to perfect foresight cases, in both price
response and wholesale market bidding setting with various energy storage
durations. We also test a transfer learning approach by pre-training the
bidding model using New York data and applying it to arbitrage in Queensland,
Australia. The result shows transfer learning achieves exceptional arbitrage
profitability with as little as three days of local training data,
demonstrating its significant advantage over training from scratch in scenarios
with very limited data availability
Using Battery Storage for Peak Shaving and Frequency Regulation: Joint Optimization for Superlinear Gains
We consider using a battery storage system simultaneously for peak shaving
and frequency regulation through a joint optimization framework which captures
battery degradation, operational constraints and uncertainties in customer load
and regulation signals. Under this framework, using real data we show the
electricity bill of users can be reduced by up to 15\%. Furthermore, we
demonstrate that the saving from joint optimization is often larger than the
sum of the optimal savings when the battery is used for the two individual
applications. A simple threshold real-time algorithm is proposed and achieves
this super-linear gain. Compared to prior works that focused on using battery
storage systems for single applications, our results suggest that batteries can
achieve much larger economic benefits than previously thought if they jointly
provide multiple services.Comment: To Appear in IEEE Transaction on Power System
Energy Storage State-of-Charge Market Model
This paper introduces and rationalizes a new model for bidding and clearing
energy storage resources in wholesale energy markets. Charge and discharge bids
in this model are dependent on the storage state-of-charge (SoC). In this
setting, storage participants submit different bids for each SoC segment. The
system operator monitors the storage SoC, and updates their bids accordingly in
market clearings. Combined with an optimal bidding design algorithm using
dynamic programming, our paper shows that the SoC segment model provides more
accurate representations of the opportunity costs of energy storage compared to
existing power-based bidding models, and captures the inherent nonlinear
operational characteristics of energy storage. We benchmark the SoC segment
model against an existing single-segment storage model in both price-taker and
price-influencer simulations. The simulation results show that the proposed
model improves profits by 10-60% and reduces system cost by 5% in comparison to
the existing power-based bidding model, and helps reduce price volatilities
Techno-Economic Analysis and Optimal Control of Battery Storage for Frequency Control Services, Applied to the German Market
Optimal investment in battery energy storage systems, taking into account
degradation, sizing and control, is crucial for the deployment of battery
storage, of which providing frequency control is one of the major applications.
In this paper, we present a holistic, data-driven framework to determine the
optimal investment, size and controller of a battery storage system providing
frequency control. We optimised the controller towards minimum degradation and
electricity costs over its lifetime, while ensuring the delivery of frequency
control services compliant with regulatory requirements. We adopted a detailed
battery model, considering the dynamics and degradation when exposed to actual
frequency data. Further, we used a stochastic optimisation objective while
constraining the probability on unavailability to deliver the frequency control
service. Through a thorough analysis, we were able to decrease the amount of
data needed and thereby decrease the execution time while keeping the
approximation error within limits. Using the proposed framework, we performed a
techno-economic analysis of a battery providing 1 MW capacity in the German
primary frequency control market. Results showed that a battery rated at 1.6
MW, 1.6 MWh has the highest net present value, yet this configuration is only
profitable if costs are low enough or in case future frequency control prices
do not decline too much. It transpires that calendar ageing drives battery
degradation, whereas cycle ageing has less impact.Comment: Submitted to Applied Energ
Evaluation of photovoltaic storage systems on energy markets under uncertainty using stochastic dynamic programming
The rising share of intermittent renewable energy production in energy systems increasingly poses a threat to system stability and the price level in energy markets. However, the effects of renewable energy production onto electricity markets also give rise to new business opportunities. The expected increase in price differences increases the market potential for storage applications and combinations with renewable energy production. The value of storage depends critically on the operation of the storage system.
In this study, we evaluate large-scale photovoltaic (PV) storage systems under uncertainty, as renewable energy production and electricity prices are fundamentally uncertain. In comparison to households who largely consume the stored energy themselves, the major business case for large-scale PV and storage systems is arbitrage trading on the electricity markets. The operation problem is formulated as a Markov decision process (MDP). Uncertainties of renewable energy production are integrated into an electricity price model using ARIMA-type approaches and regime switching. Due to non-stationarity and heteroskedasticity of the underlying processes, an appropriate stochastic modeling procedure is developed. The MDP is solved using stochastic dynamic programming (SDP) and recombining trees (RT) to reduce complexity taking into account the different time scales in which decisions have to be taken. We evaluate the solution of the SDP problem against Monte Carlo simulations with perfect foresight and against a storage dispatch heuristic. The program is applied to the German electricity and reserve power market to show the potential increase in storage value with higher price spreads, and evaluate a possible imposition of the feed-in levy onto energy directly stored from the common grid
Evaluation of photovoltaic storage systems on energy markets under uncertainty using stochastic dynamic programming
The rising share of intermittent renewable energy production in energy systems increasingly poses a threat to system stability and the price level in energy markets. However, the effects of renewable energy production onto electricity markets also give rise to new business opportunities. The expected increase in price differences increases the market potential for storage applications and combinations with renewable energy production. The value of storage depends critically on the operation of the storage system.
In this study, we evaluate large-scale photovoltaic (PV) storage systems under uncertainty, as renewable energy production and electricity prices are fundamentally uncertain. In comparison to households who largely consume the stored energy themselves, the major business case for large-scale PV and storage systems is arbitrage trading on the electricity markets. The operation problem is formulated as a Markov decision process (MDP). Uncertainties of renewable energy production are integrated into an electricity price model using ARIMA-type approaches and regime switching. Due to non-stationarity and heteroskedasticity of the underlying processes, an appropriate stochastic modeling procedure is developed. The MDP is solved using stochastic dynamic programming (SDP) and recombining trees (RT) to reduce complexity taking into account the different time scales in which decisions have to be taken. We evaluate the solution of the SDP problem against Monte Carlo simulations with perfect foresight and against a storage dispatch heuristic. The program is applied to the German electricity and reserve power market to show the potential increase in storage value with higher price spreads, and evaluate a possible imposition of the feed-in levy onto energy directly stored from the common grid
Towards near 100% renewable power systems: Improving the role of distributed energy resources using optimization models
The envisioned near 100 % renewable Power Systems, crucial in attaining the sustainability goals aspired by society, will call for the active and multifaceted participation of all the actors involved in the energy systems.
Time-varying renewable energy systems (vRES), such as solar photovoltaic (PV) and wind, will play a decisive role in meeting the ambitious renewable targets. This is due to the large availability of natural resources and the rapid decrease in investment costs observed in the last two decades. In fact, most of the scenarios to achieve near 100% RES in Europe strongly rely on these two energy sources. However, the high temporal and spatial variability of the power generated by these technologies represents a challenge for preserving the high-security standards of supply, quality of service, and the robustness of current power systems, especially with the foreseen contributions from vRES.
With an emphasis on the vital role these renewable technologies play in this process, this work aims to develop new methods and tools that may assist different players in different stages of this transition. The three leading contributions are:
1. A Multiyear Expansion-Planning Optimization Method (MEPOM) to be used in the planning processes carried out by system operators and governmental entities.
2. An Optimal Design and Sizing of Hybrid Power Plants (OptHy) decision-support tool to be used in accessing investment decisions and other managing actions led by renewable power plant owners and investors.
3. A Decision-aid Algorithm for Market Participation and Optimal Bidding Strategy (OptiBID) that market agents may adopt to operate and value their renewable energy assets in the electricity markets
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